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In 2003, the Princeton psychologist Alexander Todorov began to suspect that, except for those people who have hard-core political beliefs, the reasons we vote for particular candidates could have less to do with politics and more to do with basic cognitive processes—in particular, perception. When people are asked about their ideal leader, one of the single most important characteristics that they say they look for is competence—how qualified and capable a candidate is. Todorov wondered whether that judgment was made on the basis of intuitive responses to basic facial features rather than on any deep, rational calculus. It would make sense: in the past, extensive research has shown just how quickly we form impressions of people’s character traits, even before we’ve had a conversation with them. That impression then colors whatever else we learn about them, from their hobbies to, presumably, their political abilities. In other words, when we think that we are making rational political judgments, we could be, in fact, judging someone at least partly based on a fleeting impression of his or her face.

Starting that fall, and through the following spring, Todorov showed pairs of portraits to roughly a thousand people, and asked them to rate the competence of each person. Unbeknownst to the test subjects, they were looking at candidates for the House and Senate in 2000, 2002, and 2004. In study after study, participants’ responses to the question of whether someone looked competent predicted actual election outcomes at a rate much higher than chance—from sixty-six to seventy-three per cent of the time. Even looking at the faces for as little as one second, Todorov found, yielded the exact same result: a snap judgment that generally identified the winners and losers.

A famous slogan in statistics is that correlation does not imply causation. We know that there is a statistical correlation between eating ice cream and drowning incidents, for instance, but ice cream consumption does not cause drowning. Where any two factors –  A and B – are correlated, there are four possibilities: 1. A is a cause of B, 2. B is a cause of A, 3. the correlation is pure coincidence and 4., as in the ice cream case, A and B are connected by a common cause. Increased ice cream consumption and drowning rates both have a common cause in warm summer weather.

We know that smoking causes cancer. But we also know that many people who smoke don’t get cancer. Causal claims are not falsified by counterexamples, not even by a whole bunch of them. Contraceptive pills have been shown to cause thrombosis, but only in 1 of 1000 women. Following Popper, we could say that for every case where the cause is followed by the effect there are 999 counterexamples. Instead of falsifying the hypothesis that the pill causes thrombosis, however, we list thrombosis as a known side-effect. Causation is still very much assumed even though it occurs only in rare cases.

One could understand a cause, for instance, as a tendency towards its effect. Smoking has a tendency towards cancer, but it doesn’t guarantee it.. Contraception pills have a tendency towards thrombosis but a relatively small one. However, being hit by a train strongly tends towards death. We see that tendencies come in degrees, as do causes, some strongly tending towards their effect and some only weakly.

The borderlines between genuine science and pseudoscience may be fuzzy, but this should be even more of a call for careful distinctions, based on systematic facts and sound reasoning. To try a modicum of turtle blood here and a little aspirin there is not the hallmark of wisdom and even-mindedness. It is a dangerous gateway to superstition and irrationality.